Abstract

Visual surveillance in dynamic scenes, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to ﬁght against terrorism, crime, public safety and for eﬃcient management of traﬃc. The work involves designing of eﬃcient visual surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object classiﬁcation, target tracking, activity recognition, and behavior understanding. Detection of moving objects in video streams is the ﬁrst relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated diﬀerent background subtraction methods to overcome the problem of illumination variation, background clutter, shadows, and camouﬂage. Object classiﬁcation is done using silhouette template based classiﬁcation to categorize objects into human, group of human and vehicle. Detecting and tracking of human body parts is important in understanding human activities. We have proposed two methods to overcome the problem of object tracking in varying illumination condition and background clutter. For target tracking of interested object in the consecutive video frames, we have used normalized correlation coeﬃcient (NCC). NCC is robust to varying illumination condition. Template is updated on every frame to minimize the template drift problem and it also tries to cope with short-lived occlusion and background clutter. In order to extend the surveillance area and overcome occlusion, fusion of data from multiple cameras is employed in our project. We have tracked objects across multiple cameras with non-overlapping FOVs based on object appearances. A brightness transfer function (BTF) is determined from the cumulative histograms of the images. Matching of the object is done, with the help of Bhattacharya distance.